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Paddy insect identification using deep features with lion optimization algorithm
M A Elmagzoub1, Wahidur Rahman2,3, Kaniz Roksana2
1Department of Network and Communication Engineering, College of Computer Science and Information Systems, Najran University, Najran, 61441, Saudi Arabia.
Heliyon
|July 8, 2024
Summary
Early detection of paddy insects using advanced AI can prevent significant rice yield losses. This study integrates Deep Learning and Machine Learning with feature optimization for accurate pest identification.
Area of Science:
- Agricultural Science
- Computer Science
- Artificial Intelligence
Background:
- Pests cause substantial global rice yield losses, estimated at 20%.
- Early detection of paddy insects is crucial for mitigating these economic impacts.
- Existing insect identification systems lack integrated feature optimization with Deep Learning and Machine Learning.
Purpose of the Study:
- To develop a framework for prompt detection and categorization of paddy insects using advanced AI techniques.
- To enhance paddy insect image datasets through pre-processing and feature selection.
- To improve the accuracy and efficiency of paddy insect diagnosis in agricultural fields.
Main Methods:
- Gathering and categorizing a paddy insect image dataset.
- Applying pre-processing techniques like augmentation and image filtering.
- Utilizing 5 pre-trained Convolutional Neural Network models for feature extraction.
- Implementing feature selection methods: Principal Component Analysis (PCA), Recursive Feature Elimination (RFE), Linear Discriminant Analysis (LDA), and Lion Optimization.
- Employing 7 Machine Learning algorithms for insect identification.
Main Results:
- The proposed framework successfully detects and categorizes paddy insects from images.
- Feature vectors extracted using ResNet50 combined with Logistic Regression and PCA achieved the highest accuracy of 99.28%.
- The integration of feature optimization techniques significantly improved diagnostic capabilities.
Conclusions:
- The developed AI framework offers a highly accurate and efficient solution for paddy insect diagnosis.
- This approach has the potential to significantly reduce crop losses in paddy cultivation.
- The study highlights the importance of combining Deep Learning, Machine Learning, and feature optimization for agricultural pest management.
